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## Loading required package: stringr
## Loading required package: XML
## 
## Attaching package: 'acs'
## The following object is masked from 'package:base':
## 
##     apply
## Loading required package: ggplot2
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
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##     filter
## The following object is masked from 'package:graphics':
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##     layout
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::combine() masks acs::combine()
## ✖ dplyr::filter()  masks plotly::filter(), stats::filter()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
#census_api_key('250b627b4881bc26920e76c0ef71f0ae699bd2ba')
bpl_or <- get_acs(
  geography = 'tract',
  year = 2021,
  survey = 'acs5',
  variables = c(pov = 'B17001_002'),
  state = "OR",
  county = 'Multnomah County'
) 
homeownership_or <- get_acs(
  geography = 'tract',
  year = 2021,
  survey = 'acs5',
  variables = c(home_ownership = 'B25003_002'),
  state = "OR",
  county = 'Multnomah County'
) 
library(ggplot2)
bpl_vs_houseownership <- homeownership_or %>%
  select(NAME, estimate) %>%
  rename(home_ownership = estimate)
pop_md_education <- bpl_or %>%
  select(NAME, estimate) %>%
  rename(pov = estimate)
bpl_vs_houseownership <- full_join(homeownership_or, bpl_or, by = "NAME")
alldata <- get_acs(
  geography = 'tract',
  year = 2021,
  survey = 'acs5',
  variables = c(home_ownership = 'B25003_002',
                pov = 'B17001_002',
                pop = 'B01001_001'
                ),
  state = "OR",
  county = 'Multnomah County',
  output = "wide"
) %>% 
  rename(
    home_ownership = home_ownershipE,
    poverty = povE,
    population = popE
  )

selectedvariables <- alldata %>% 
  select(NAME, home_ownership, poverty, population) %>% 
  mutate(
    percentpov = poverty / population,
    percenthomeownership = home_ownership / population,
    PopulationSize = case_when(
      population < 2000 ~ "Small",
      population > 2000 & population < 5000 ~ "Medium",
      population > 5000 ~ "Large"
    )
  )
selectedvariables <- selectedvariables[-197,]
#palette <- rev(brewer.pal(n = 3, name = "Set1"))

fig <- ggplot(selectedvariables, aes(x = percenthomeownership, y = percentpov, label = NAME)) +
geom_point() +
geom_point(aes(color = PopulationSize))+
geom_smooth(method = "lm") +
labs(x = "Percentage Homeownership", y = "Percentage Poverty") +
  ggtitle("Poverty Vs Home Ownership") + #scale_color_manual(values=c('Navy','Skyblue', 'Lightblue'))
  scale_color_brewer(palette = "YlOrRd", direction=-1)

ggplotly(fig)
## `geom_smooth()` using formula = 'y ~ x'
## Warning: The following aesthetics were dropped during statistical transformation: label
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?

The above plot concludes that there is a causality between Poverty and Home ownership. A steep downward trend is observed. It demonstrates that as the percentage poverty reduces the percentage home ownership increases. The red points represent the neighborhoods with high population occupancy while the yellow points depict the neighborhoods with low population occupancy. The red points are mostly clustered in the middle area which represents that people with moderate income tend to own homes. Detailed information about each point can be obtained by hovering on them. Therefore it can be concluded that there is a correlation between poverty and home ownership. As the poverty reduces the probability of owning a house increases.